Simulation of log fitness change
Laure Olazcuaga, Nicolas Rode
01-December-2020
1 DATA
## Longitudinal data
data_sum <- loadlongitudinaldata(dataset = "DATA_Adults_G1G29.csv", rm_generation1 = 1,rm_generation2 = 7,rm_generation3 = 29)
## Phenotyping steps
data_G0 <- loadfitnessdata(dataset = "Selection_Phenotypage_G0_G7_G8.csv", generation = "G1")
data_G7 <- loadfitnessdata(dataset = "Selection_Phenotypage_G0_G7_G8.csv", generation = "G7")
data_G29 <- loadfitnessdata(dataset =
"PERFORMANCE_Comptage_adultes_G13G14G15G16G17G18G19G20G21G22G23G24G25G26G27G28G29.csv",
generation = "29")
head(data_sum)## Line Fruit_s Generation Phase N Nb_adults sd fitness
## 1 CE1 Cherry 2 first_prepool 20 9.15000 6.123939 -0.7819784
## 2 CE1 Cherry 3 first_prepool 10 15.30000 7.631077 -0.2678794
## 3 CE1 Cherry 4 first_prepool 8 15.00000 7.782765 -0.2876821
## 4 CE1 Cherry 5 first_prepool 6 14.50000 6.284903 -0.3215836
## 5 CE2 Cherry 2 first_prepool 20 7.75000 7.758696 -0.9480394
## 6 CE2 Cherry 3 first_prepool 7 14.42857 8.303757 -0.3265219
## se_fitness
## 1 0.1496562
## 2 0.1577228
## 3 0.1834415
## 4 0.1769518
## 5 0.2238577
## 6 0.2175215head(data_G0)## Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 993 Cherry CE1 GF 76 6 0 0.07894737
## 994 Cherry CE1 GF 89 17 0 0.19101124
## 995 Cherry CE1 GF 57 12 0 0.21052632
## 996 Cherry CE1 GF 172 24 0 0.13953488
## 997 Cherry CE1 GF 173 33 0 0.19075145
## 998 Cherry CE1 GF 91 18 0 0.19780220head(data_G7)## Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 3 Strawberry CR4 Cranberry 152 68 0 0.4473684
## 4 Cranberry CR4 Cranberry 246 25 1 0.1016260
## 5 Cherry CR4 Cranberry 238 29 0 0.1218487
## 6 Cherry CR4 Cranberry 166 23 0 0.1385542
## 8 Cranberry FR3 Strawberry 204 5 0 0.0245098
## 9 Strawberry FR3 Strawberry 124 45 1 0.3629032head(data_G29)## Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 5392 Strawberry CEA Cherry 196 16 0 0.08163265
## 5393 Strawberry CEA Cherry 192 30 0 0.15625000
## 5394 Strawberry CEA Cherry 160 17 0 0.10625000
## 5395 Strawberry CEA Cherry 106 9 0 0.08490566
## 5396 Strawberry CEA Cherry 119 14 0 0.11764706
## 5397 Strawberry CEA Cherry 204 24 0 0.11764706dim(data_G29)## [1] 990 7#Compute log change
data_logchange <- computelogchange(fitness_dataset_intermediate = data_G7, fitness_dataset_final = data_G29)
#Lines: CE2 and CR2 do not pass Geary's test (the threshold of a standardized mean greater than 3)
data_logchange$Line_type <- ifelse(data_logchange$Line == "CR2"| data_logchange$Line == "CE2", "solid","dashed")
#Formatting for testing correlation
TEMP_dataG7_CheCran <- formattinglogchange(data_logchange, "7", "Cherry", "Cranberry")
TEMP_dataG7_CranStraw <- formattinglogchange(data_logchange, "7", "Cranberry", "Strawberry")
TEMP_dataG7_StrawChe <- formattinglogchange(data_logchange, "7", "Strawberry", "Cherry")
TEMP_dataG29_CheCran <- formattinglogchange(data_logchange, "29", "Cherry", "Cranberry")
TEMP_dataG29_CranStraw <- formattinglogchange(data_logchange, "29", "Cranberry", "Strawberry")
TEMP_dataG29_StrawChe <- formattinglogchange(data_logchange, "29", "Strawberry", "Cherry")2 ALL FRUITS
## Longitudinal data
data_sum_allfruits <- loadlongitudinaldata_allfruits(dataset = "DATA_Adults_G1G29.csv", rm_generation1 = 1,rm_generation2 = 7,rm_generation3 = 29)
data_sum_allfruits## Line Fruit_s Generation Phase N Nb_adults sd
## 1 CA1 Blackcurrant 2 first_prepool 20 2.3500000 3.2971279
## 2 CA1 Blackcurrant 3 first_prepool 2 0.5000000 0.7071068
## 3 CA2 Blackcurrant 2 first_prepool 11 1.0000000 1.5491933
## 4 CA3 Blackcurrant 2 first_prepool 12 2.0833333 2.6097138
## 5 CA3 Blackcurrant 3 first_prepool 1 1.0000000 NA
## 6 CA4 Blackcurrant 2 first_prepool 12 1.6666667 1.4974726
## 7 CA4 Blackcurrant 3 first_prepool 1 0.0000000 NA
## 8 CA5 Blackcurrant 2 first_prepool 12 2.1666667 2.5166115
## 9 CA5 Blackcurrant 3 first_prepool 1 5.0000000 NA
## 10 CE1 Cherry 2 first_prepool 20 9.1500000 6.1239392
## 11 CE1 Cherry 3 first_prepool 10 15.3000000 7.6310768
## 12 CE1 Cherry 4 first_prepool 8 15.0000000 7.7827648
## 13 CE1 Cherry 5 first_prepool 6 14.5000000 6.2849025
## 14 CE2 Cherry 2 first_prepool 20 7.7500000 7.7586963
## 15 CE2 Cherry 3 first_prepool 7 14.4285714 8.3037570
## 16 CE2 Cherry 4 first_prepool 5 15.4000000 7.8930349
## 17 CE2 Cherry 5 first_prepool 4 8.2500000 4.3493295
## 18 CE3 Cherry 2 first_prepool 20 8.7000000 8.2020537
## 19 CE3 Cherry 3 first_prepool 8 20.3750000 9.7532046
## 20 CE3 Cherry 4 first_prepool 9 19.1111111 7.0079320
## 21 CE3 Cherry 5 first_prepool 8 15.3750000 10.9013433
## 22 CE4 Cherry 2 first_prepool 20 12.5000000 6.8402832
## 23 CE4 Cherry 3 first_prepool 12 16.3333333 9.4034165
## 24 CE4 Cherry 4 first_prepool 10 12.5000000 6.8190908
## 25 CE4 Cherry 5 first_prepool 6 9.6666667 3.9327683
## 26 CE5 Cherry 2 first_prepool 20 5.9000000 5.6837719
## 27 CE5 Cherry 3 first_prepool 5 5.4000000 6.0249481
## 28 CE5 Cherry 4 first_prepool 1 16.0000000 NA
## 29 CR1 Cranberry 2 first_prepool 20 19.7000000 7.4275448
## 30 CR1 Cranberry 3 first_prepool 20 9.5000000 6.6054204
## 31 CR1 Cranberry 4 first_prepool 10 8.5000000 4.1432676
## 32 CR1 Cranberry 5 first_prepool 4 5.0000000 8.0415587
## 33 CR2 Cranberry 2 first_prepool 20 13.6500000 8.4559229
## 34 CR2 Cranberry 3 first_prepool 15 13.2666667 8.9479979
## 35 CR2 Cranberry 4 first_prepool 10 9.6000000 7.5159091
## 36 CR2 Cranberry 5 first_prepool 5 10.8000000 5.8906706
## 37 CR3 Cranberry 2 first_prepool 20 25.1000000 6.2314989
## 38 CR3 Cranberry 3 first_prepool 20 17.0500000 10.5355289
## 39 CR3 Cranberry 4 first_prepool 17 13.1176471 7.7126710
## 40 CR3 Cranberry 5 first_prepool 11 12.8181818 10.0081785
## 41 CR4 Cranberry 2 first_prepool 20 19.6500000 9.2524534
## 42 CR4 Cranberry 3 first_prepool 20 16.8000000 11.3211493
## 43 CR4 Cranberry 4 first_prepool 16 6.5000000 8.5401015
## 44 CR4 Cranberry 5 first_prepool 5 5.6000000 7.5365775
## 45 CR5 Cranberry 2 first_prepool 18 18.8333333 8.7464278
## 46 CR5 Cranberry 3 first_prepool 16 6.4375000 5.7383941
## 47 CR5 Cranberry 4 first_prepool 5 11.0000000 5.1478151
## 48 CR5 Cranberry 5 first_prepool 3 9.0000000 5.5677644
## 49 EG1 Rosehips 2 first_prepool 7 0.5714286 0.5345225
## 50 EG2 Rosehips 2 first_prepool 12 0.6666667 0.8876254
## 51 EG3 Rosehips 2 first_prepool 7 2.2857143 2.6903708
## 52 EG4 Rosehips 2 first_prepool 11 5.0000000 5.9160798
## 53 EG4 Rosehips 3 first_prepool 2 9.5000000 10.6066017
## 54 EG4 Rosehips 4 first_prepool 1 19.0000000 NA
## 55 EG5 Rosehips 2 first_prepool 18 3.3333333 4.2702803
## 56 EG5 Rosehips 3 first_prepool 2 9.5000000 0.7071068
## 57 EG5 Rosehips 4 first_prepool 1 1.0000000 NA
## 58 FI1 Fig 2 first_prepool 20 9.2000000 9.6223424
## 59 FI1 Fig 3 first_prepool 9 18.1111111 4.6755867
## 60 FI1 Fig 4 first_prepool 8 7.8750000 6.2435681
## 61 FI2 Fig 2 first_prepool 20 15.1500000 7.4992982
## 62 FI2 Fig 3 first_prepool 15 7.1333333 6.8333914
## 63 FI2 Fig 4 first_prepool 6 3.5000000 4.3243497
## 64 FI3 Fig 2 first_prepool 20 11.8500000 5.7241409
## 65 FI3 Fig 3 first_prepool 11 8.7272727 5.3682569
## 66 FI3 Fig 4 first_prepool 5 2.4000000 4.2778499
## 67 FI4 Fig 2 first_prepool 11 5.8181818 5.7239529
## 68 FI4 Fig 3 first_prepool 3 7.0000000 12.1243557
## 69 FI4 Fig 4 first_prepool 1 1.0000000 NA
## 70 FI5 Fig 2 first_prepool 13 3.8461538 6.5300096
## 71 FI5 Fig 3 first_prepool 2 11.0000000 4.2426407
## 72 FI5 Fig 4 first_prepool 1 2.0000000 NA
## 73 FR1 Strawberry 2 first_prepool 20 16.8500000 7.0582166
## 74 FR1 Strawberry 3 first_prepool 18 16.1111111 8.5052848
## 75 FR1 Strawberry 4 first_prepool 15 16.2000000 7.6082662
## 76 FR1 Strawberry 5 first_prepool 11 25.8181818 10.9162098
## 77 FR2 Strawberry 2 first_prepool 20 7.7500000 7.0178344
## 78 FR2 Strawberry 3 first_prepool 7 11.8571429 9.4238805
## 79 FR2 Strawberry 4 first_prepool 5 16.6000000 10.1390335
## 80 FR2 Strawberry 5 first_prepool 5 6.4000000 7.4027022
## 81 FR3 Strawberry 2 first_prepool 20 14.0500000 11.4131734
## 82 FR3 Strawberry 3 first_prepool 13 14.6923077 6.0880294
## 83 FR3 Strawberry 4 first_prepool 10 11.8000000 10.7061768
## 84 FR3 Strawberry 5 first_prepool 5 9.4000000 7.7974355
## 85 FR4 Strawberry 2 first_prepool 20 14.1000000 7.0404844
## 86 FR4 Strawberry 3 first_prepool 15 12.0000000 8.1940745
## 87 FR4 Strawberry 4 first_prepool 9 16.7777778 13.7638819
## 88 FR4 Strawberry 5 first_prepool 8 13.8750000 10.8947891
## 89 FR5 Strawberry 2 first_prepool 20 8.2000000 8.2945765
## 90 FR5 Strawberry 3 first_prepool 7 7.1428571 7.8619033
## 91 FR5 Strawberry 4 first_prepool 2 9.0000000 9.8994949
## 92 FR5 Strawberry 5 first_prepool 1 9.0000000 NA
## 93 RA1 Grape 2 first_prepool 8 3.3750000 2.5035689
## 94 RA1 Grape 3 first_prepool 1 5.0000000 NA
## 95 RA2 Grape 2 first_prepool 5 0.6000000 1.3416408
## 96 RA3 Grape 2 first_prepool 5 2.8000000 3.1144823
## 97 RA4 Grape 2 first_prepool 5 0.6000000 1.3416408
## 98 RA5 Grape 2 first_prepool 9 2.0000000 2.5000000
## 99 TO1 Tomato 2 first_prepool 7 5.1428571 9.4415495
## 100 TO1 Tomato 3 first_prepool 3 6.0000000 6.2449980
## 101 TO1 Tomato 4 first_prepool 1 12.0000000 NA
## 102 TO2 Tomato 2 first_prepool 2 8.0000000 9.8994949
## 103 TO2 Tomato 3 first_prepool 1 7.0000000 NA
## 104 TO3 Tomato 2 first_prepool 9 4.1111111 7.5571894
## 105 TO3 Tomato 3 first_prepool 1 21.0000000 NA
## 106 TO3 Tomato 4 first_prepool 1 5.0000000 NA
## 107 TO4 Tomato 2 first_prepool 2 0.5000000 0.7071068
## 108 TO5 Tomato 2 first_prepool 3 1.6666667 2.8867513
## fitness se_fitness
## 1 -2.14131695 0.31372775
## 2 -3.68887945 1.00000000
## 3 -2.99573227 0.46709937
## 4 -2.26176310 0.36161255
## 5 -2.99573227 NA
## 6 -2.48490665 0.25936987
## 7 -Inf NA
## 8 -2.22254239 0.33529992
## 9 -1.38629436 NA
## 10 -0.78197839 0.14965622
## 11 -0.26787945 0.15772277
## 12 -0.28768207 0.18344153
## 13 -0.32158362 0.17695177
## 14 -0.94803943 0.22385771
## 15 -0.32652191 0.21752155
## 16 -0.26136476 0.22921250
## 17 -0.88551907 0.26359572
## 18 -0.83240925 0.21080862
## 19 0.01857639 0.16924067
## 20 -0.04546237 0.12223137
## 21 -0.26298946 0.25068012
## 22 -0.47000363 0.12236271
## 23 -0.20252426 0.16619587
## 24 -0.47000363 0.17251087
## 25 -0.72704873 0.16609096
## 26 -1.22077992 0.21541187
## 27 -1.30933332 0.49897013
## 28 -0.22314355 NA
## 29 -0.01511364 0.08430708
## 30 -0.74444047 0.15547546
## 31 -0.85566611 0.15414309
## 32 -1.38629436 0.80415587
## 33 -0.38199275 0.13852028
## 34 -0.41047765 0.17414797
## 35 -0.73396918 0.24757699
## 36 -0.61618614 0.24392481
## 37 0.22713557 0.05551416
## 38 -0.15958207 0.13817102
## 39 -0.42177385 0.14260160
## 40 -0.44486766 0.23541399
## 41 -0.01765494 0.10528812
## 42 -0.17435339 0.15068369
## 43 -1.12393010 0.32846544
## 44 -1.27296568 0.60186784
## 45 -0.06010392 0.10946298
## 46 -1.13359201 0.22285026
## 47 -0.59783700 0.20928844
## 48 -0.79850770 0.35717225
## 49 -3.55534806 0.35355339
## 50 -3.40119738 0.38435306
## 51 -2.16905370 0.44487826
## 52 -1.38629436 0.35675303
## 53 -0.74444047 0.78947368
## 54 -0.05129329 NA
## 55 -1.79175947 0.30195442
## 56 -0.74444047 0.05263158
## 57 -2.99573227 NA
## 58 -0.77652879 0.23387187
## 59 -0.09920665 0.08605374
## 60 -0.93203909 0.28030917
## 61 -0.27773174 0.11068608
## 62 -1.03095364 0.24734216
## 63 -1.74296931 0.50440239
## 64 -0.52340441 0.10801323
## 65 -0.82927935 0.18546348
## 66 -2.12026354 0.79713027
## 67 -1.23474446 0.29662819
## 68 -1.04982212 1.00000000
## 69 -2.99573227 NA
## 70 -1.64865863 0.47088569
## 71 -0.59783700 0.27272727
## 72 -2.30258509 NA
## 73 -0.17138162 0.09366559
## 74 -0.21622311 0.12443058
## 75 -0.21072103 0.12126209
## 76 0.25534669 0.12748230
## 77 -0.94803943 0.20248200
## 78 -0.52280181 0.30040053
## 79 -0.18632958 0.27315142
## 80 -1.13943428 0.51727954
## 81 -0.35310988 0.18164151
## 82 -0.30840820 0.11492514
## 83 -0.52763274 0.28691444
## 84 -0.75502258 0.37097012
## 85 -0.34955748 0.11165249
## 86 -0.51082562 0.17630841
## 87 -0.17567701 0.27345461
## 88 -0.36564361 0.27761367
## 89 -0.89159812 0.22618582
## 90 -1.02961942 0.41601282
## 91 -0.79850770 0.77777778
## 92 -0.79850770 NA
## 93 -1.77933695 0.26226526
## 94 -1.38629436 NA
## 95 -3.50655790 1.00000000
## 96 -1.96611286 0.49744244
## 97 -3.50655790 1.00000000
## 98 -2.30258509 0.41666667
## 99 -1.35812348 0.69388867
## 100 -1.20397280 0.60092521
## 101 -0.51082562 NA
## 102 -0.91629073 0.87500000
## 103 -1.04982212 NA
## 104 -1.58203894 0.61274508
## 105 0.04879016 NA
## 106 -1.38629436 NA
## 107 -3.68887945 1.00000000
## 108 -2.48490665 1.00000000pd <- position_dodge(0.3) # move them .05 to the left and right
PLOT_ALLFRUITS<-ggplot(data=data_sum_allfruits,
aes(x=factor(Generation),group=Line, y=fitness, colour=Fruit_s)) +
facet_wrap(~ Fruit_s, nrow = 8) +
geom_errorbar(aes(ymin=fitness-1.96*se_fitness, ymax=fitness+1.96*se_fitness),
width=.1,position=pd,size=0.2,color="black") +
geom_line(size=0.3,position=pd) +
geom_point(size=1, position=pd,shape=21, fill="white") +
ylab("Fitness") +
xlab("Generation") +
theme_LO_sober
PLOT_ALLFRUITS
cowplot::save_plot(file =here::here("figures", "PLOT_AllFruits.pdf"), PLOT_ALLFRUITS, base_height = 17/cm(1), base_width = 15/cm(1), dpi = 1200)
###########Check CA4
data_sum_allfruits<-data_sum_allfruits[data_sum_allfruits$Line!="CA4",]
#Models
mod_all_interaction <- lm(fitness ~ Fruit_s*Generation,
weights = N, data = data_sum_allfruits)
mod_all_fruit_generation<- lm(fitness ~ Fruit_s+Generation ,
weights = N, data = data_sum_allfruits)
mod_all_fruit<- lm(fitness ~ Fruit_s ,
weights = N, data = data_sum_allfruits)
mod_all_null<- lm(fitness ~ 1 ,
weights = N, data = data_sum_allfruits)
MuMIn::AICc(mod_all_interaction,mod_all_fruit_generation,mod_all_fruit,mod_all_null)## df AICc
## mod_all_interaction 17 202.1323
## mod_all_fruit_generation 10 207.8578
## mod_all_fruit 9 205.7438
## mod_all_null 2 301.0742#Posthoc
emmeans::emmeans(mod_all_interaction, list(pairwise ~ Fruit_s), adjust = "tukey") ### $`emmeans of Fruit_s`
## Fruit_s emmean SE df lower.CL upper.CL
## Blackcurrant -3.012 0.8241 90 -5.313 -0.7104
## Cherry -0.562 0.1062 90 -0.859 -0.2660
## Cranberry -0.444 0.0887 90 -0.692 -0.1965
## Fig -1.069 0.1532 90 -1.497 -0.6412
## Grape -1.250 1.6478 90 -5.851 3.3509
## Rosehips -1.429 0.4782 90 -2.764 -0.0936
## Strawberry -0.439 0.0972 90 -0.710 -0.1677
## Tomato -1.111 0.4618 90 -2.401 0.1783
##
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 8 estimates
##
## $`pairwise differences of Fruit_s`
## contrast estimate SE df t.ratio p.value
## Blackcurrant - Cherry -2.44904 0.831 90 -2.947 0.0752
## Blackcurrant - Cranberry -2.56745 0.829 90 -3.098 0.0508
## Blackcurrant - Fig -1.94262 0.838 90 -2.318 0.2958
## Blackcurrant - Grape -1.76127 1.842 90 -0.956 0.9793
## Blackcurrant - Rosehips -1.58273 0.953 90 -1.661 0.7118
## Blackcurrant - Strawberry -2.57259 0.830 90 -3.100 0.0504
## Blackcurrant - Tomato -1.90029 0.945 90 -2.012 0.4801
## Cherry - Cranberry -0.11841 0.138 90 -0.856 0.9891
## Cherry - Fig 0.50641 0.186 90 2.717 0.1309
## Cherry - Grape 0.68777 1.651 90 0.417 0.9999
## Cherry - Rosehips 0.86631 0.490 90 1.769 0.6425
## Cherry - Strawberry -0.12355 0.144 90 -0.858 0.9889
## Cherry - Tomato 0.54875 0.474 90 1.158 0.9415
## Cranberry - Fig 0.62483 0.177 90 3.530 0.0146
## Cranberry - Grape 0.80618 1.650 90 0.489 0.9997
## Cranberry - Rosehips 0.98472 0.486 90 2.025 0.4713
## Cranberry - Strawberry -0.00513 0.132 90 -0.039 1.0000
## Cranberry - Tomato 0.66716 0.470 90 1.419 0.8463
## Fig - Grape 0.18135 1.655 90 0.110 1.0000
## Fig - Rosehips 0.35989 0.502 90 0.717 0.9963
## Fig - Strawberry -0.62996 0.181 90 -3.473 0.0173
## Fig - Tomato 0.04233 0.487 90 0.087 1.0000
## Grape - Rosehips 0.17854 1.716 90 0.104 1.0000
## Grape - Strawberry -0.81132 1.651 90 -0.492 0.9997
## Grape - Tomato -0.13902 1.711 90 -0.081 1.0000
## Rosehips - Strawberry -0.98986 0.488 90 -2.029 0.4688
## Rosehips - Tomato -0.31756 0.665 90 -0.478 0.9997
## Strawberry - Tomato 0.67230 0.472 90 1.425 0.8435
##
## P value adjustment: tukey method for comparing a family of 8 estimatessummary(mod_all_interaction)##
## Call:
## lm(formula = fitness ~ Fruit_s * Generation, data = data_sum_allfruits,
## weights = N)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -3.7941 -0.9849 0.1232 0.9455 3.0501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.1887 1.5830 -0.751 0.455
## Fruit_sCherry 0.0652 1.6115 0.040 0.968
## Fruit_sCranberry 1.6444 1.6091 1.022 0.310
## Fruit_sFig 1.0779 1.6442 0.656 0.514
## Fruit_sGrape -3.5250 3.4243 -1.029 0.306
## Fruit_sRosehips -2.6798 1.8532 -1.446 0.152
## Fruit_sStrawberry 0.4937 1.6093 0.307 0.760
## Fruit_sTomato -1.6412 1.9193 -0.855 0.395
## Generation -0.5837 0.7600 -0.768 0.444
## Fruit_sCherry:Generation 0.7634 0.7662 0.996 0.322
## Fruit_sCranberry:Generation 0.2956 0.7652 0.386 0.700
## Fruit_sFig:Generation 0.2769 0.7780 0.356 0.723
## Fruit_sGrape:Generation 1.6929 1.6728 1.012 0.314
## Fruit_sRosehips:Generation 1.3650 0.8799 1.551 0.124
## Fruit_sStrawberry:Generation 0.6657 0.7654 0.870 0.387
## Fruit_sTomato:Generation 1.1341 0.8869 1.279 0.204
##
## Residual standard error: 1.468 on 90 degrees of freedom
## Multiple R-squared: 0.7222, Adjusted R-squared: 0.6759
## F-statistic: 15.6 on 15 and 90 DF, p-value: < 2.2e-163 ADAPTATION
3.1 Proportion of adaptation: raw data
tapply(data_sum$Nb_adults[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)## Cherry Cranberry Strawberry
## 8.80000 19.38667 12.19000tapply(data_sum$Nb_adults[data_sum$Generation=="27"],data_sum$Fruit_s[data_sum$Generation=="27"],mean)## Cherry Cranberry Strawberry
## 35.84000 33.63750 42.33333## Calcul proportion change between G2 and G7
((tapply(data_sum$Nb_adults[data_sum$Generation=="27"],data_sum$Fruit_s[data_sum$Generation=="27"],mean) -
tapply(data_sum$Nb_adults[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)) /
tapply(data_sum$Nb_adults[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)) * 100## Cherry Cranberry Strawberry
## 307.27273 73.50843 247.27919#
# (abs(tapply(data_sum$fitness[data_sum$Generation=="27"],data_sum$Fruit_s[data_sum$Generation=="27"],mean) -
# tapply(data_sum$fitness[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)) /
# abs(tapply(data_sum$fitness[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean))) * 1003.2 Analysis
######## Models
mod_nul <- lme4::lmer(fitness ~ 1 + (1|Generation),
weights = N, data = data_sum)
mod_Fruits <- lme4::lmer(fitness ~ Fruit_s + (1|Generation),
weights = N, data = data_sum)
mod_Generation <- lme4::lmer(fitness ~ Generation + (1|Generation),
weights = N, data = data_sum)
mod_Phase <- lme4::lmer(fitness~ Phase + (1|Generation),
weights = N, data = data_sum)
mod_Lines <- lme4::lmer(fitness~ Line + (1|Generation),
weights = N, data = data_sum)
mod_Generation_Phase <- lme4::lmer(fitness~ Phase + Generation + (1|Generation),
weights = N, data = data_sum)
mod_Generation_Fruits <- lme4::lmer(fitness ~ Generation + Fruit_s + (1|Generation),
weights = N, data = data_sum)
mod_Fruits_Phase <- lme4::lmer(fitness ~ Phase + Fruit_s + (1|Generation),
weights = N, data = data_sum)
mod_Generation_Fruits_Phase <- lme4::lmer(fitness ~ Generation + Phase + Fruit_s + (1|Generation),
weights = N, data = data_sum)
mod_Doubleinteraction <- lme4::lmer(fitness ~ Generation + Phase + Fruit_s +
Fruit_s:Generation +
(1|Generation),
weights = N, data = data_sum)
mod_Double_gene <- lme4::lmer(fitness ~ Generation*Fruit_s +
(1|Generation),
weights = N, data = data_sum)
mod_Double <- lme4::lmer(fitness ~ Phase*Fruit_s +
(1|Generation),
weights = N, data = data_sum)
mod_Double_Generation <- lme4::lmer(fitness ~ Generation + Phase*Fruit_s +
(1|Generation),
weights = N, data = data_sum)
mod_Tripleinteraction <- lme4::lmer(fitness ~ Generation*Phase*Fruit_s +
(1|Generation),
weights = N, data = data_sum)
mod_DoublePopulationGeneration <- lme4::lmer(fitness ~ Generation*Line +
(1|Generation),
weights = N, data = data_sum)
# Add indic
data_sum$indic_cherry<- ifelse(data_sum$Fruit_s=="Cherry", "Cherry", "Others")
data_sum$indic_cranberry <- ifelse(data_sum$Fruit_s=="Cranberry", "Cranberry", "Others")
data_sum$indic_strawberry <- ifelse(data_sum$Fruit_s=="Strawberry", "Strawberry", "Others")
# Test whether the temporal dynamics are different in one fruit compared to the two other fruits
mod_Cherry_Phase <- lme4::lmer(fitness ~ Phase*indic_cherry + (1|Generation),
weights = N, data = data_sum)
mod_Cranberry_Phase <- lme4::lmer(fitness ~ Phase*indic_cranberry + (1|Generation),
weights = N, data = data_sum)
mod_Strawberry_Phase <- lme4::lmer(fitness ~ Phase*indic_strawberry + (1|Generation),
weights = N, data = data_sum)
# Model selection: AICC and Loglik
MuMIn::AICc(mod_nul,mod_Fruits,mod_Generation,mod_Phase,mod_Lines,mod_Generation_Phase,mod_Generation_Fruits,mod_Fruits_Phase,mod_Generation_Fruits_Phase,mod_Double_gene,mod_Doubleinteraction,mod_Double,mod_Double_Generation,mod_Tripleinteraction,mod_DoublePopulationGeneration,mod_Cherry_Phase,mod_Cranberry_Phase,mod_Strawberry_Phase)## df AICc
## mod_nul 3 339.7756
## mod_Fruits 5 333.0398
## mod_Generation 4 334.4473
## mod_Phase 5 314.8770
## mod_Lines 31 366.1793
## mod_Generation_Phase 6 322.8487
## mod_Generation_Fruits 6 328.4534
## mod_Fruits_Phase 7 309.1298
## mod_Generation_Fruits_Phase 8 317.0215
## mod_Double_gene 8 342.2655
## mod_Doubleinteraction 10 330.9505
## mod_Double 11 317.2153
## mod_Double_Generation 12 325.1311
## mod_Tripleinteraction 20 354.5837
## mod_DoublePopulationGeneration 60 504.0109
## mod_Cherry_Phase 8 307.8003
## mod_Cranberry_Phase 8 328.4648
## mod_Strawberry_Phase 8 316.4444MuMIn::model.sel(mod_nul,mod_Fruits,mod_Generation,mod_Phase,mod_Lines,mod_Generation_Phase,mod_Generation_Fruits,mod_Fruits_Phase,mod_Generation_Fruits_Phase,mod_Double_gene,mod_Doubleinteraction,mod_Double,mod_Double_Generation,mod_Tripleinteraction,mod_DoublePopulationGeneration,mod_Cherry_Phase,mod_Cranberry_Phase,mod_Strawberry_Phase)## Model selection table
## (Int) Frt_s Gnr Phs Lin Frt_s:Gnr
## mod_Cherry_Phase -0.6076 +
## mod_Fruits_Phase -0.3364 + +
## mod_Phase -0.4877 +
## mod_Strawberry_Phase -0.5050 +
## mod_Generation_Fruits_Phase -0.2913 + -0.0134300 +
## mod_Double -0.6078 + +
## mod_Generation_Phase -0.4455 -0.0127800 +
## mod_Double_Generation -0.5626 + -0.0137100 +
## mod_Generation_Fruits -0.1599 + 0.0388900
## mod_Cranberry_Phase -0.4319 +
## mod_Doubleinteraction -0.4975 + -0.0007595 + +
## mod_Fruits 0.4343 +
## mod_Generation -0.3267 0.0398800
## mod_nul 0.2798
## mod_Double_gene -0.3736 + 0.0517500 +
## mod_Tripleinteraction -1.1070 + 0.1752000 + +
## mod_Lines -0.5347 +
## mod_DoublePopulationGeneration -1.0460 0.1785000 +
## Frt_s:Phs Gnr:Phs Frt_s:Gnr:Phs Gnr:Lin ind_chr
## mod_Cherry_Phase +
## mod_Fruits_Phase
## mod_Phase
## mod_Strawberry_Phase
## mod_Generation_Fruits_Phase
## mod_Double +
## mod_Generation_Phase
## mod_Double_Generation +
## mod_Generation_Fruits
## mod_Cranberry_Phase
## mod_Doubleinteraction
## mod_Fruits
## mod_Generation
## mod_nul
## mod_Double_gene
## mod_Tripleinteraction + + +
## mod_Lines
## mod_DoublePopulationGeneration +
## ind_chr:Phs ind_crn ind_crn:Phs ind_str
## mod_Cherry_Phase +
## mod_Fruits_Phase
## mod_Phase
## mod_Strawberry_Phase +
## mod_Generation_Fruits_Phase
## mod_Double
## mod_Generation_Phase
## mod_Double_Generation
## mod_Generation_Fruits
## mod_Cranberry_Phase + +
## mod_Doubleinteraction
## mod_Fruits
## mod_Generation
## mod_nul
## mod_Double_gene
## mod_Tripleinteraction
## mod_Lines
## mod_DoublePopulationGeneration
## ind_str:Phs family df logLik AICc
## mod_Cherry_Phase gaussian(identity) 8 -145.598 307.8
## mod_Fruits_Phase gaussian(identity) 7 -147.331 309.1
## mod_Phase gaussian(identity) 5 -152.314 314.9
## mod_Strawberry_Phase + gaussian(identity) 8 -149.920 316.4
## mod_Generation_Fruits_Phase gaussian(identity) 8 -150.208 317.0
## mod_Double gaussian(identity) 11 -147.046 317.2
## mod_Generation_Phase gaussian(identity) 6 -155.249 322.8
## mod_Double_Generation gaussian(identity) 12 -149.899 325.1
## mod_Generation_Fruits gaussian(identity) 6 -158.052 328.5
## mod_Cranberry_Phase gaussian(identity) 8 -155.930 328.5
## mod_Doubleinteraction gaussian(identity) 10 -155.009 331.0
## mod_Fruits gaussian(identity) 5 -161.395 333.0
## mod_Generation gaussian(identity) 4 -163.141 334.4
## mod_nul gaussian(identity) 3 -166.838 339.8
## mod_Double_gene gaussian(identity) 8 -162.830 342.3
## mod_Tripleinteraction gaussian(identity) 20 -155.433 354.6
## mod_Lines gaussian(identity) 31 -147.476 366.2
## mod_DoublePopulationGeneration gaussian(identity) 60 -172.328 504.0
## delta weight
## mod_Cherry_Phase 0.00 0.634
## mod_Fruits_Phase 1.33 0.326
## mod_Phase 7.08 0.018
## mod_Strawberry_Phase 8.64 0.008
## mod_Generation_Fruits_Phase 9.22 0.006
## mod_Double 9.41 0.006
## mod_Generation_Phase 15.05 0.000
## mod_Double_Generation 17.33 0.000
## mod_Generation_Fruits 20.65 0.000
## mod_Cranberry_Phase 20.66 0.000
## mod_Doubleinteraction 23.15 0.000
## mod_Fruits 25.24 0.000
## mod_Generation 26.65 0.000
## mod_nul 31.98 0.000
## mod_Double_gene 34.47 0.000
## mod_Tripleinteraction 46.78 0.000
## mod_Lines 58.38 0.000
## mod_DoublePopulationGeneration 196.21 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## '1 | Generation'anova(mod_nul,mod_Fruits,mod_Generation,mod_Phase,mod_Lines,mod_Generation_Phase,mod_Generation_Fruits,mod_Fruits_Phase,mod_Generation_Fruits_Phase,mod_Doubleinteraction,mod_Double,mod_Double_Generation,mod_Tripleinteraction,mod_DoublePopulationGeneration,mod_Double_gene,mod_Cherry_Phase,mod_Cranberry_Phase,mod_Strawberry_Phase)## Data: data_sum
## Models:
## mod_nul: fitness ~ 1 + (1 | Generation)
## mod_Generation: fitness ~ Generation + (1 | Generation)
## mod_Fruits: fitness ~ Fruit_s + (1 | Generation)
## mod_Phase: fitness ~ Phase + (1 | Generation)
## mod_Generation_Phase: fitness ~ Phase + Generation + (1 | Generation)
## mod_Generation_Fruits: fitness ~ Generation + Fruit_s + (1 | Generation)
## mod_Fruits_Phase: fitness ~ Phase + Fruit_s + (1 | Generation)
## mod_Generation_Fruits_Phase: fitness ~ Generation + Phase + Fruit_s + (1 | Generation)
## mod_Double_gene: fitness ~ Generation * Fruit_s + (1 | Generation)
## mod_Cherry_Phase: fitness ~ Phase * indic_cherry + (1 | Generation)
## mod_Cranberry_Phase: fitness ~ Phase * indic_cranberry + (1 | Generation)
## mod_Strawberry_Phase: fitness ~ Phase * indic_strawberry + (1 | Generation)
## mod_Doubleinteraction: fitness ~ Generation + Phase + Fruit_s + Fruit_s:Generation +
## mod_Doubleinteraction: (1 | Generation)
## mod_Double: fitness ~ Phase * Fruit_s + (1 | Generation)
## mod_Double_Generation: fitness ~ Generation + Phase * Fruit_s + (1 | Generation)
## mod_Tripleinteraction: fitness ~ Generation * Phase * Fruit_s + (1 | Generation)
## mod_Lines: fitness ~ Line + (1 | Generation)
## mod_DoublePopulationGeneration: fitness ~ Generation * Line + (1 | Generation)
## npar AIC BIC logLik deviance Chisq Df
## mod_nul 3 336.65 347.18 -165.33 330.65
## mod_Generation 4 323.01 337.05 -157.50 315.01 15.6458 1
## mod_Fruits 5 321.74 339.29 -155.87 311.74 3.2688 1
## mod_Phase 5 305.39 322.93 -147.69 295.39 16.3510 0
## mod_Generation_Phase 6 305.95 327.01 -146.98 293.95 1.4362 1
## mod_Generation_Fruits 6 308.84 329.90 -148.42 296.84 0.0000 0
## mod_Fruits_Phase 7 291.48 316.05 -138.74 277.48 19.3583 1
## mod_Generation_Fruits_Phase 8 291.95 320.02 -137.97 275.95 1.5325 1
## mod_Double_gene 8 306.40 334.48 -145.20 290.40 0.0000 0
## mod_Cherry_Phase 8 290.29 318.37 -137.15 274.29 16.1075 0
## mod_Cranberry_Phase 8 310.11 338.19 -147.06 294.11 0.0000 0
## mod_Strawberry_Phase 8 298.37 326.45 -141.19 282.37 11.7394 0
## mod_Doubleinteraction 10 289.56 324.65 -134.78 269.56 12.8159 2
## mod_Double 11 291.19 329.79 -134.59 269.19 0.3679 1
## mod_Double_Generation 12 291.62 333.73 -133.81 267.62 1.5683 1
## mod_Tripleinteraction 20 290.03 360.22 -125.02 250.03 17.5887 8
## mod_Lines 31 304.85 413.64 -121.42 242.85 7.1828 11
## mod_DoublePopulationGeneration 60 322.24 532.80 -101.12 202.24 40.6128 29
## Pr(>Chisq)
## mod_nul
## mod_Generation 7.638e-05 ***
## mod_Fruits 0.070610 .
## mod_Phase < 2.2e-16 ***
## mod_Generation_Phase 0.230756
## mod_Generation_Fruits 1.000000
## mod_Fruits_Phase 1.083e-05 ***
## mod_Generation_Fruits_Phase 0.215736
## mod_Double_gene 1.000000
## mod_Cherry_Phase < 2.2e-16 ***
## mod_Cranberry_Phase 1.000000
## mod_Strawberry_Phase < 2.2e-16 ***
## mod_Doubleinteraction 0.001648 **
## mod_Double 0.544137
## mod_Double_Generation 0.210447
## mod_Tripleinteraction 0.024530 *
## mod_Lines 0.784096
## mod_DoublePopulationGeneration 0.074405 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Posthoc
mod_Best <- lme4::lmer(fitness ~ Phase*indic_cherry + (1|Generation),
weights = N, data = data_sum)
emmeans::emmeans(mod_Best, list(pairwise ~ Phase:indic_cherry), adjust = "tukey") ### $`emmeans of Phase, indic_cherry`
## Phase indic_cherry emmean SE df lower.CL upper.CL
## first_prepool Cherry -0.607592 0.1631 674 -1.038 -0.177
## pool Cherry 0.321562 0.1674 111144 -0.119 0.762
## second_postpool Cherry 0.701026 0.0712 2598 0.514 0.889
## first_prepool Others -0.442650 0.1288 340 -0.783 -0.102
## pool Others 0.000256 0.1106 107399 -0.291 0.291
## second_postpool Others 0.455445 0.0585 1121 0.301 0.610
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 6 estimates
##
## $`pairwise differences of Phase, indic_cherry`
## contrast estimate SE df t.ratio
## first_prepool Cherry - pool Cherry -0.929 0.2337 2618 -3.976
## first_prepool Cherry - second_postpool Cherry -1.309 0.1779 796 -7.354
## first_prepool Cherry - first_prepool Others -0.165 0.1484 27353 -1.111
## first_prepool Cherry - pool Others -0.608 0.1970 1380 -3.085
## first_prepool Cherry - second_postpool Others -1.063 0.1733 710 -6.135
## pool Cherry - second_postpool Cherry -0.379 0.1819 38241 -2.086
## pool Cherry - first_prepool Others 0.764 0.2112 2298 3.619
## pool Cherry - pool Others 0.321 0.1479 504858 2.173
## pool Cherry - second_postpool Others -0.134 0.1773 34407 -0.755
## second_postpool Cherry - first_prepool Others 1.144 0.1471 475 7.773
## second_postpool Cherry - pool Others 0.701 0.1315 17346 5.328
## second_postpool Cherry - second_postpool Others 0.246 0.0614 125672 3.997
## first_prepool Others - pool Others -0.443 0.1697 999 -2.609
## first_prepool Others - second_postpool Others -0.898 0.1414 401 -6.350
## pool Others - second_postpool Others -0.455 0.1251 13913 -3.638
## p.value
## 0.0010
## <.0001
## 0.8770
## 0.0253
## <.0001
## 0.2946
## 0.0041
## 0.2505
## 0.9748
## <.0001
## <.0001
## 0.0009
## 0.0959
## <.0001
## 0.0037
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 6 estimatessummary(mod_Best)## Linear mixed model fit by REML ['lmerMod']
## Formula: fitness ~ Phase * indic_cherry + (1 | Generation)
## Data: data_sum
## Weights: N
##
## REML criterion at convergence: 291.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7742 -0.4669 0.1121 0.5198 2.5009
##
## Random effects:
## Groups Name Variance Std.Dev.
## Generation (Intercept) 0.03638 0.1907
## Residual 3.11306 1.7644
## Number of obs: 247, groups: Generation, 24
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.6076 0.1604 -3.788
## Phasepool 0.9292 0.2318 4.008
## Phasesecond_postpool 1.3086 0.1753 7.465
## indic_cherryOthers 0.1649 0.1483 1.112
## Phasepool:indic_cherryOthers -0.4862 0.2094 -2.322
## Phasesecond_postpool:indic_cherryOthers -0.4105 0.1605 -2.557
##
## Correlation of Fixed Effects:
## (Intr) Phaspl Phssc_ indc_O Phs:_O
## Phasepool -0.692
## Phsscnd_pst -0.915 0.633
## indc_chrryO -0.670 0.464 0.613
## Phspl:ndc_O 0.475 -0.716 -0.434 -0.708
## Phsscnd_:_O 0.619 -0.428 -0.663 -0.924 0.654################### BELOW TEMP: A SUPR
############### Percent of Increase in fitness
# Calcul_increase_fitness <- expand.grid(Phase = unique(data_sum$Phase),
# Fruit_s = unique(data_sum$Fruit_s))
#
# Calcul_increase_fitness$fitness_predicted <- predict(mod_Best,
# newdata = Calcul_increase_fitness,
# re.form= NA, type = "response")
#
# ((Calcul_increase_fitness$fitness_predicted[Calcul_increase_fitness$Phase=="second_postpool"]-
# Calcul_increase_fitness$fitness_predicted[Calcul_increase_fitness$Phase=="first_prepool"])/
# abs(Calcul_increase_fitness$fitness_predicted[Calcul_increase_fitness$Phase=="first_prepool"]))*100
#
# Calcul_increase_fitness3.3 Plot
#Position dodge
pd <- ggplot2::position_dodge(0.3) # move them .05 to the left and right
#Extract slope and intercept
dat_predict_allfruits <- expand.grid(Generation =as.numeric(levels(as.factor(data_sum$Generation))),
Fruit_s=unique(data_sum$Fruit_s))
dat_predict_allfruits$Phase <- ifelse(dat_predict_allfruits$Generation<= 7, "first_prepool",
ifelse(dat_predict_allfruits$Generation>=12, "second_postpool", "pool"))
dat_predict_allfruits$indic_cherry<- ifelse(dat_predict_allfruits$Fruit_s=="Cherry", "Cherry", "Others")
dat_predict_allfruits$fitness_predicted <- predict(mod_Best, newdata = dat_predict_allfruits,
re.form= NA, type = "response")
#REAL DATA
#Add G1 G6 and G7
TEMP_lineG1 <- c(rep(NA, 2), 1,rep(NA, 5))
TEMP_lineG6 <- c(rep(NA, 2), 6,rep(NA, 5))
TEMP_lineG7 <- c(rep(NA, 2), 7,rep(NA, 5))
TEMP_total <- rbind(data_sum,TEMP_lineG1,TEMP_lineG1,TEMP_lineG1,TEMP_lineG6,TEMP_lineG6,TEMP_lineG6,TEMP_lineG7,TEMP_lineG7,TEMP_lineG7)
TEMP_total$Fruit_s[TEMP_total$Generation == "6"] <- c("Strawberry", "Cranberry", "Cherry")
TEMP_total$Fruit_s[TEMP_total$Generation == "1"] <- c("Strawberry", "Cranberry", "Cherry")
TEMP_total$Fruit_s[TEMP_total$Generation == "7"] <- c("Strawberry", "Cranberry", "Cherry")
tail(TEMP_total)## Line Fruit_s Generation Phase N Nb_adults sd fitness se_fitness
## 251 <NA> Strawberry 6 <NA> NA NA NA NA NA
## 252 <NA> Cranberry 6 <NA> NA NA NA NA NA
## 253 <NA> Cherry 6 <NA> NA NA NA NA NA
## 254 <NA> Strawberry 7 <NA> NA NA NA NA NA
## 255 <NA> Cranberry 7 <NA> NA NA NA NA NA
## 256 <NA> Cherry 7 <NA> NA NA NA NA NA
## indic_cherry indic_cranberry indic_strawberry
## 251 <NA> 6 <NA>
## 252 <NA> 6 <NA>
## 253 <NA> 6 <NA>
## 254 <NA> 7 <NA>
## 255 <NA> 7 <NA>
## 256 <NA> 7 <NA>## Add label
TEMP_anno <- data.frame(x1 = c(3.5, 3.5, 3.5, 10, 3.5, 10),
x2 = c(9, 17.5, 17.5, 17.5, 17.5, 17.5),
y1 = c(1, 2, 2, 1.2, 2, 1.2),
y2 = c(1.25, 2.25, 2.25, 1.45, 2.25, 1.45),
xstar = c(6.5, 10, 10, 14, 10, 14),
ystar = c(1.5, 2.5, 2.5, 1.7, 2.5, 1.7),
lab = c("**", "***", "***", "**", "***", "**"),
Fruit_s = c("Cherry", "Cherry",
"Cranberry", "Cranberry",
"Strawberry", "Strawberry"),
Line = NA)
TEMP_anno## x1 x2 y1 y2 xstar ystar lab Fruit_s Line
## 1 3.5 9.0 1.0 1.25 6.5 1.5 ** Cherry NA
## 2 3.5 17.5 2.0 2.25 10.0 2.5 *** Cherry NA
## 3 3.5 17.5 2.0 2.25 10.0 2.5 *** Cranberry NA
## 4 10.0 17.5 1.2 1.45 14.0 1.7 ** Cranberry NA
## 5 3.5 17.5 2.0 2.25 10.0 2.5 *** Strawberry NA
## 6 10.0 17.5 1.2 1.45 14.0 1.7 ** Strawberry NATEMP_title <- data.frame(xtitle = c(14, 14, 14),
ytitle = c(3, 3, 3),
title = c("Cherry", "Cranberry", "Strawberry"),
Fruit_s = c("Cherry", "Cranberry", "Strawberry"),
Line = NA)
TEMP_title## xtitle ytitle title Fruit_s Line
## 1 14 3 Cherry Cherry NA
## 2 14 3 Cranberry Cranberry NA
## 3 14 3 Strawberry Strawberry NAPLOT_FITNESS_CHERRY <- ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Cherry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Cherry",],
aes(x = factor(Generation), y = fitness_predicted,
colour = "black", group = Phase), size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x1, xend = x1,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x2, xend = x2,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x1, xend = x2,
y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#BC3C6D", "#FDB424", "#3FAA96")) +
ggtitle("Cherry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#BC3C6D"))
PLOT_FITNESS_CHERRY
PLOT_FITNESS_CRANB <- ggplot2::ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Cranberry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Cranberry",], aes(x = factor(Generation), y = fitness_predicted, colour = "black", group = Phase),
size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x1, xend = x1,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x2, xend = x2,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x1, xend = x2,
y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#FDB424")) +
ggtitle("Cranberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#FDB424"))
PLOT_FITNESS_CRANB
PLOT_FITNESS_STRAW <- ggplot2::ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Strawberry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Strawberry",],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group = Phase),
size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x1, xend = x1, y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x2, xend = x2, y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x1, xend = x2, y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#3FAA96")) +
ggtitle("Strawberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#3FAA96"))
PLOT_FITNESS_STRAW
DYNAMIQUE_THREE_JOIN <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_FITNESS_CHERRY + theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.66, width = 1, height = 0.33) +
cowplot::draw_plot(PLOT_FITNESS_CRANB + theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.33, width = 1, height = 0.33) +
cowplot::draw_plot(PLOT_FITNESS_STRAW,
x = 0, y = 00, width = 1, height = 0.33) +
cowplot::draw_plot_label(c("A", "B", "C"),
x = c(0, 0, 0),
y = c(1, 0.66, 0.33),
hjust = c(-0.5, -0.5, -0.5),
vjust = c(1.5, 1.5, 1.5),
size = 12)
DYNAMIQUE_THREE_JOIN
cowplot::save_plot(file =here::here("figures", "FIG_Adaptation.pdf"), DYNAMIQUE_THREE_JOIN, base_height = 17/cm(1), base_width = 11/cm(1), dpi = 1200)4 HETEROGENEITY
4.1 Plot
pd <- position_dodge(width = 0.5)
################### INTERMEDIATE PHENOTYPING
# Re-order levels of Line
data_sum_G7 <- data_logchange[data_logchange$Generation == "7",]
data_sum_G7$Line <- factor(data_sum_G7$Line, levels= c("CE2", "CE1", "CE4", "CE3",
"CR2", "CR3", "CR5", "CR1", "CR4",
"FR2", "FR3", "FR5", "FR1", "FR4"))
#Plot:
symp_allop_g7 <- ggplot(data = data_sum_G7,
aes(x = Line, y = logchange, group = Treatment,
color = Fruit_s, shape = Treatment, fill = Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_errorbar(aes(ymin = logchange-1.96*sd_logchange,
ymax =logchange + 1.96*sd_logchange, linetype = Line_type),
width = 0.2, size = 0.5, alpha = 0.6,position = pd) +
geom_point(position = pd, fill = "white", size =3) +
ylab("Fitness difference between intermediate\nand initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(color = FALSE,
shape = guide_legend(override.aes = list(fill = c("black")))) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 15),clip = "off") +
ggtitle("Intermediate phenotyping step") +
annotate("text", x = 2.5, y =1.5, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 7.5, y =1.5, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 12.5, y =1.5, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE) +
geom_segment(x = 15.5, y = -0.1, xend= 15.5, yend = -1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
geom_segment(x = 15.5, y = 0.1, xend= 15.5, yend = 1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
annotate("text", x = 16.2, y = 0.5, label = 'bold(" Fitness\nincrease")',
size =3,colour = "black",parse = TRUE, angle =90) +
annotate("text", x = 16.2, y =-0.5, label = 'bold(" Fitness\ndecrease")',
size =3,colour = "black",parse = TRUE, angle =90)
symp_allop_g7
################### FINAL PHENOTYPING
# Re-order levels of Line
data_sum_G29 <- data_logchange[data_logchange$Generation == "29",]
data_sum_G29$Line <- factor(data_sum_G29$Line, levels = c("CEA", "CEB", "CEC",
"CRD", "CRA", "CRC", "CRB", "CRE",
"FRA", "FRC", "FRB"))
symp_allop_G29 <- ggplot(data = data_sum_G29,
aes(x = Line, y=logchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_point(size =3, position = pd) +
geom_errorbar(aes(ymin =logchange-1.96*sd_logchange, ymax =logchange + 1.96*sd_logchange),
width= 0.2, size = 0.5, alpha = 0.6,position = pd) +
ylab("Fitness difference between final\nand initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(fill = FALSE, alpha = FALSE) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 11),clip = "off") +
annotate("text", x = 2, y =1.5, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 6, y =1.5, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 10, y =1.5, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE) +
geom_segment(x = 11.5, y = -0.1, xend= 11.5, yend = -1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
geom_segment(x = 11.5, y = 0.1, xend= 11.5, yend = 1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
annotate("text", x = 12, y = 0.5, label = 'bold(" Fitness\nincrease")',
size =3,colour = "black",parse = TRUE, angle =90) +
annotate("text", x = 12, y = -0.5, label = 'bold(" Fitness\ndecrease")',
size =3,colour = "black", parse = TRUE, angle =90) +
ggtitle("Final phenotyping step")
symp_allop_G29
### Add stroke
DIF_FITNESS_G7 <- symp_allop_g7 +
geom_point(aes(alpha = SA, color = interaction(SA,Fruit_s)), position = pd, size =3, stroke =1.5, fill = "white") +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96", "#7C2748", "#CA8702", "#328677", "#BC3C6D", "#FDB424", "#3FAA96"))
DIF_FITNESS_G7
DIF_FITNESS_G29 <- symp_allop_G29 + geom_point(aes(alpha = SA, color =interaction(SA,Fruit_s)), position = pd, size =3, stroke =1.5) +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96", "#7C2748", "#CA8702", "#328677", "#BC3C6D", "#FDB424", "#3FAA96"))
DIF_FITNESS_G29
plot_legend <- ggplot(data = data_logchange, aes(x = Line, y=logchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_point(size =3, position = pd) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober +
guides(fill = FALSE) +
theme(legend.key.size = unit(0.5, "cm"),
axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 11),clip = "off")
###########################################################
#############################################ALL PLOT
legend_trade <- lemon::g_legend(plot_legend)
SYMP_ALLOP_TOTAL <- cowplot::ggdraw() +
cowplot::draw_plot(DIF_FITNESS_G7 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0.5, width = 1, height = 0.5) +
cowplot::draw_plot(DIF_FITNESS_G29 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0, width = 1, height = 0.5) +
cowplot::draw_plot_label(c("A", "B"),
x = c(0, 0), y = c(1, 0.5),
size = 14) +
cowplot::draw_plot(legend_trade, x = 0.93, y = 0.5, width = 0.001, height = 0.001)
SYMP_ALLOP_TOTAL
cowplot::save_plot(file =here::here("figures", "FIG_Heterogeneity.pdf"),
SYMP_ALLOP_TOTAL, base_height = 20/cm(1), base_width = 20/cm(1), dpi = 1200)5 CORRELATED RESPONSES
5.1 Analysis MA and correlation
# ##### Create empty dataframe with slopes and confidence interval for all pairwise
Estimates_pairwise <- data.frame(Variables = rep(rep(c("correlation","cor_CI_inf","cor_CI_sup",
"slope", "intercept"),2),3),
Generation = rep(c(rep(7, 5), rep(29, 5)),3),
Pairwise = rep(c("Cherry_Cranberry",
"Cranberry_Strawberry",
"Strawberry_Cherry"), each=10),
Estimates = NA )
## Nbr sample
nb_sim = 500
#########################
######################### G7
#########################
#######
####### Cherry Cranberry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG7_CheCran)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="correlation"] <- mean(sim$correlation.cor, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="cor_CI_inf"] <- quantile(sim$correlation.cor, probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="cor_CI_sup"] <- quantile(sim$correlation.cor, probs = 0.975, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="intercept"] <- mean(sim$intercept, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="slope"] <- mean(sim$slope, na.rm = TRUE)
#######
####### Cranberry Strawberry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG7_CranStraw)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="correlation"] <- quantile(sim$cor, probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="cor_CI_inf"] <- quantile(sim$cor, probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="cor_CI_sup"] <- quantile(sim$cor, probs = 0.975, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
#######
####### Strawberry Cherry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG7_StrawChe)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="correlation"] <- quantile(sim$cor, probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="cor_CI_inf"] <- quantile(sim$cor, probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="cor_CI_sup"] <- quantile(sim$cor, probs = 0.975, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
#########################
######################### G29
#########################
#######
####### Cherry Cranberry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG29_CheCran)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="correlation"] <- quantile(sim$cor, probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="cor_CI_inf"] <- quantile(sim$cor, probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="cor_CI_sup"] <- quantile(sim$cor, probs = 0.975, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept, probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope, probs = 0.5, na.rm = TRUE)
#######
####### Cranberry Strawberry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG29_CranStraw)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="correlation"] <- quantile(sim$cor, probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="cor_CI_inf"] <- quantile(sim$cor, probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="cor_CI_sup"] <- quantile(sim$cor, probs = 0.975, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
#######
####### Strawberry Cherry
#######
rm(sim)
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG29_StrawChe)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="correlation"] <- quantile(sim$cor, probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="cor_CI_inf"] <- quantile(sim$cor, probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="cor_CI_sup"] <- quantile(sim$cor, probs = 0.975, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
Estimates_pairwise## Variables Generation Pairwise Estimates
## 1 correlation 7 Cherry_Cranberry 0.51235824
## 2 cor_CI_inf 7 Cherry_Cranberry -0.26985112
## 3 cor_CI_sup 7 Cherry_Cranberry 0.98513182
## 4 slope 7 Cherry_Cranberry 4.78676276
## 5 intercept 7 Cherry_Cranberry -0.89344304
## 6 correlation 29 Cherry_Cranberry -0.66081010
## 7 cor_CI_inf 29 Cherry_Cranberry -0.98341734
## 8 cor_CI_sup 29 Cherry_Cranberry 0.03114952
## 9 slope 29 Cherry_Cranberry -1.58942013
## 10 intercept 29 Cherry_Cranberry 0.23790858
## 11 correlation 7 Cranberry_Strawberry 0.66688074
## 12 cor_CI_inf 7 Cranberry_Strawberry 0.21569369
## 13 cor_CI_sup 7 Cranberry_Strawberry 0.92784591
## 14 slope 7 Cranberry_Strawberry 1.24112298
## 15 intercept 7 Cranberry_Strawberry -0.04634633
## 16 correlation 29 Cranberry_Strawberry 0.60888143
## 17 cor_CI_inf 29 Cranberry_Strawberry 0.09598369
## 18 cor_CI_sup 29 Cranberry_Strawberry 0.93016894
## 19 slope 29 Cranberry_Strawberry 1.44473566
## 20 intercept 29 Cranberry_Strawberry -0.48298442
## 21 correlation 7 Strawberry_Cherry 0.66109984
## 22 cor_CI_inf 7 Strawberry_Cherry 0.05864868
## 23 cor_CI_sup 7 Strawberry_Cherry 0.99009143
## 24 slope 7 Strawberry_Cherry 1.16489520
## 25 intercept 7 Strawberry_Cherry 0.05721628
## 26 correlation 29 Strawberry_Cherry 0.72302682
## 27 cor_CI_inf 29 Strawberry_Cherry 0.38397534
## 28 cor_CI_sup 29 Strawberry_Cherry 0.99729944
## 29 slope 29 Strawberry_Cherry 2.51811211
## 30 intercept 29 Strawberry_Cherry -0.795533555.2 Plot
ymin = -50
ymax = 50
##################################################
################## G7
# Limits
ymin_CheCranG7=min(min(TEMP_dataG7_CheCran$logchange_allop-1.96*TEMP_dataG7_CheCran$sd_allop, na.rm= TRUE),
min(TEMP_dataG7_CheCran$logchange_symp-1.96*TEMP_dataG7_CheCran$sd_symp, na.rm= TRUE))
ymax_CheCranG7=max(max(TEMP_dataG7_CheCran$logchange_allop + 1.96*TEMP_dataG7_CheCran$sd_allop, na.rm= TRUE),
max(TEMP_dataG7_CheCran$logchange_symp + 1.96*TEMP_dataG7_CheCran$sd_symp, na.rm= TRUE))
## Function equation R
eq_r <- function(gen = 7, pair = "Cherry_Cranberry"){
eq <- substitute(~~italic(r)^2~"="~r2,
list(r2 = format(Estimates_pairwise$Estimates[Estimates_pairwise$Generation == gen &
Estimates_pairwise$Pairwise == pair &
Estimates_pairwise$Variables == "correlation"], digits = 2)))
as.character(as.expression(eq));
}
# Plot
CheCran_G7 <- ggplot(data = TEMP_dataG7_CheCran) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_text(x = 0.7, y = -1.2, label = eq_r(gen = 7, pair = "Cherry_Cranberry"), parse = TRUE, color="black") +
geom_errorbar(aes(x = logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp, linetype = Line_type),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp, linetype = Line_type),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x = logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size = 3, fill = "white", stroke =1.2) +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Cherry vs. Cranberry") +
theme_LO_sober +
labs(shape = "Test fruit", color = "Evolution fruit") +
scale_shape_manual(values = c(21, 22)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424")) +
coord_cartesian(ylim = c(ymin_CheCranG7, ymax_CheCranG7),
xlim = c(ymin_CheCranG7, ymax_CheCranG7))
CheCran_G7
# Limits
ymin_CranStrawG7=min(min(TEMP_dataG7_CranStraw$logchange_allop-1.96*TEMP_dataG7_CranStraw$sd_allop, na.rm= TRUE),
min(TEMP_dataG7_CranStraw$logchange_symp-1.96*TEMP_dataG7_CranStraw$sd_symp, na.rm= TRUE))
ymax_CranStrawG7=max(max(TEMP_dataG7_CranStraw$logchange_allop + 1.96*TEMP_dataG7_CranStraw$sd_allop, na.rm= TRUE),
max(TEMP_dataG7_CranStraw$logchange_symp + 1.96*TEMP_dataG7_CranStraw$sd_symp, na.rm= TRUE))
CranStraw_G7 <- ggplot(data = TEMP_dataG7_CranStraw) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp, linetype = Line_type),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp, linetype = Line_type),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_text(x = -0.25, y = -0.95, label = eq_r(gen = 7, pair = "Cranberry_Strawberry"), parse = TRUE, color="black") +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Cranberry vs. Strawberry") +
coord_cartesian(ylim = c(ymin_CranStrawG7, ymax_CranStrawG7),
xlim = c(ymin_CranStrawG7, ymax_CranStrawG7)) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(22, 24)) +
scale_color_manual(values = c("#FDB424", "#3FAA96")) +
theme_LO_sober
CranStraw_G7
# Limits
ymin_StrawCheG7=min(min(TEMP_dataG7_StrawChe$logchange_allop-1.96*TEMP_dataG7_StrawChe$sd_allop, na.rm= TRUE),
min(TEMP_dataG7_StrawChe$logchange_symp-1.96*TEMP_dataG7_StrawChe$sd_symp, na.rm= TRUE))
ymax_StrawCheG7=max(max(TEMP_dataG7_StrawChe$logchange_allop + 1.96*TEMP_dataG7_StrawChe$sd_allop, na.rm= TRUE),
max(TEMP_dataG7_StrawChe$logchange_symp + 1.96*TEMP_dataG7_StrawChe$sd_symp, na.rm= TRUE))
StrawChe_G7 <- ggplot(data = TEMP_dataG7_StrawChe) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop), ymax = logchange_allop + (1.96*sd_allop),
color = Symp, linetype = Line_type),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp, linetype = Line_type),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_text(x = 0.5, y = -1.3, label = eq_r(gen = 7, pair = "Strawberry_Cherry"), parse = TRUE, color="black") +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Strawberry vs. Cherry") +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(21, 24)) +
scale_color_manual(values = c("#BC3C6D", "#3FAA96")) +
coord_cartesian(ylim = c(ymin_StrawCheG7, ymax_StrawCheG7),
xlim = c(ymin_StrawCheG7, ymax_StrawCheG7)) +
theme_LO_sober
StrawChe_G7
#####################################################################################
################################## G29 ##################################
#####################################################################################
TEMP_dataG29_CheCran## Line Treatment Fruit_s Generation logchange sd_logchange SA Symp
## 14 CEA Cranberry Cherry 29 -0.21774071 0.13281291 0 Cherry
## 17 CEB Cranberry Cherry 29 -0.59200204 0.09634940 0 Cherry
## 21 CEC Cranberry Cherry 29 -0.20670829 0.12591289 0 Cherry
## 37 CRA Cherry Cranberry 29 0.32537794 0.10025669 0 Cranberry
## 42 CRB Cherry Cranberry 29 0.16929862 0.12699030 0 Cranberry
## 45 CRC Cherry Cranberry 29 0.08474123 0.12334598 0 Cranberry
## 47 CRD Cherry Cranberry 29 0.54736475 0.09718018 0 Cranberry
## 49 CRE Cherry Cranberry 29 -0.10496044 0.11556266 0 Cranberry
## Allop Line_type logchange_allop sd_allop logchange_symp sd_symp
## 14 Cranberry dashed -0.21774071 0.13281291 0.14767000 0.1574284
## 17 Cranberry dashed -0.59200204 0.09634940 0.25038029 0.1009864
## 21 Cranberry dashed -0.20670829 0.12591289 0.35854062 0.1169300
## 37 Cherry dashed 0.32537794 0.10025669 -0.14885350 0.1193552
## 42 Cherry dashed 0.16929862 0.12699030 0.29719689 0.1531197
## 45 Cherry dashed 0.08474123 0.12334598 0.02703717 0.1156381
## 47 Cherry dashed 0.54736475 0.09718018 -0.15627744 0.1156695
## 49 Cherry dashed -0.10496044 0.11556266 0.72513486 0.1275430TEMP_dataG29_CranStraw## Line Treatment Fruit_s Generation logchange sd_logchange SA Symp
## 39 CRA Strawberry Cranberry 29 -0.29731422 0.1098141 0 Cranberry
## 40 CRB Strawberry Cranberry 29 -0.24539416 0.1387043 0 Cranberry
## 43 CRC Strawberry Cranberry 29 -0.28950778 0.2012393 0 Cranberry
## 48 CRD Strawberry Cranberry 29 -1.02555272 0.1902082 0 Cranberry
## 51 CRE Strawberry Cranberry 29 -0.16810249 0.1398057 0 Cranberry
## 69 FRA Cranberry Strawberry 29 -0.25158762 0.1712755 0 Strawberry
## 71 FRB Cranberry Strawberry 29 0.04303751 0.1441119 0 Strawberry
## 74 FRC Cranberry Strawberry 29 0.52109773 0.1254820 0 Strawberry
## Allop Line_type logchange_allop sd_allop logchange_symp sd_symp
## 39 Strawberry dashed -0.29731422 0.1098141 -0.14885350 0.1193552
## 40 Strawberry dashed -0.24539416 0.1387043 0.29719689 0.1531197
## 43 Strawberry dashed -0.28950778 0.2012393 0.02703717 0.1156381
## 48 Strawberry dashed -1.02555272 0.1902082 -0.15627744 0.1156695
## 51 Strawberry dashed -0.16810249 0.1398057 0.72513486 0.1275430
## 69 Cranberry dashed -0.25158762 0.1712755 0.15709032 0.1411560
## 71 Cranberry dashed 0.04303751 0.1441119 0.56023100 0.1284283
## 74 Cranberry dashed 0.52109773 0.1254820 0.36640738 0.1201170TEMP_dataG29_StrawChe## Line Treatment Fruit_s Generation logchange sd_logchange SA Symp
## 13 CEA Strawberry Cherry 29 0.01971359 0.1475176 0 Cherry
## 16 CEB Strawberry Cherry 29 -0.15850680 0.0983504 0 Cherry
## 19 CEC Strawberry Cherry 29 0.21131464 0.1237821 0 Cherry
## 68 FRA Cherry Strawberry 29 -0.50599095 0.1378652 0 Strawberry
## 72 FRB Cherry Strawberry 29 0.19377147 0.1094819 0 Strawberry
## 75 FRC Cherry Strawberry 29 0.32151693 0.1140574 0 Strawberry
## Allop Line_type logchange_allop sd_allop logchange_symp sd_symp
## 13 Strawberry dashed 0.01971359 0.1475176 0.1476700 0.1574284
## 16 Strawberry dashed -0.15850680 0.0983504 0.2503803 0.1009864
## 19 Strawberry dashed 0.21131464 0.1237821 0.3585406 0.1169300
## 68 Cherry dashed -0.50599095 0.1378652 0.1570903 0.1411560
## 72 Cherry dashed 0.19377147 0.1094819 0.5602310 0.1284283
## 75 Cherry dashed 0.32151693 0.1140574 0.3664074 0.1201170# Limits
ymin_CheCranG29=min(min(TEMP_dataG29_CheCran$logchange_allop-1.96*TEMP_dataG29_CheCran$sd_allop, na.rm= TRUE),
min(TEMP_dataG29_CheCran$logchange_symp-1.96*TEMP_dataG29_CheCran$sd_symp, na.rm= TRUE))
ymax_CheCranG29=max(max(TEMP_dataG29_CheCran$logchange_allop + 1.96*TEMP_dataG29_CheCran$sd_allop, na.rm= TRUE),
max(TEMP_dataG29_CheCran$logchange_symp + 1.96*TEMP_dataG29_CheCran$sd_symp, na.rm= TRUE))
CheCran_G29 <- ggplot(data = TEMP_dataG29_CheCran) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_text(x = -0.5, y = -0.4, label = eq_r(gen = 29, pair = "Cherry_Cranberry"), parse = TRUE, color="black") +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Cherry vs. Cranberry") +
labs(shape = "Test fruit", color = "Evolution fruit") +
scale_shape_manual(values = c(16, 15)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424")) +
coord_cartesian(ylim = c(ymin_CheCranG29, ymax_CheCranG29),
xlim = c(ymin_CheCranG29, ymax_CheCranG29)) +
theme_LO_sober
CheCran_G29
# Limits
ymin_CranStrawG29=min(min(TEMP_dataG29_CranStraw$logchange_allop-1.96*TEMP_dataG29_CranStraw$sd_allop, na.rm= TRUE),
min(TEMP_dataG29_CranStraw$logchange_symp-1.96*TEMP_dataG29_CranStraw$sd_symp, na.rm= TRUE))
ymax_CranStrawG29=max(max(TEMP_dataG29_CranStraw$logchange_allop + 1.96*TEMP_dataG29_CranStraw$sd_allop, na.rm= TRUE),
max(TEMP_dataG29_CranStraw$logchange_symp + 1.96*TEMP_dataG29_CranStraw$sd_symp, na.rm= TRUE))
#Plot
CranStraw_G29 <- ggplot(data = TEMP_dataG29_CranStraw) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_text(x = -1, y = -0.8, label = eq_r(gen = 29, pair = "Cranberry_Strawberry"), parse = TRUE, color="black") +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Cranberry vs. Strawberry") +
coord_cartesian(ylim = c(ymin_CranStrawG29, ymax_CranStrawG29),
xlim = c(ymin_CranStrawG29, ymax_CranStrawG29)) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(15, 17)) +
scale_color_manual(values = c("#FDB424", "#3FAA96")) +
theme_LO_sober
CranStraw_G29
# Limits
ymin_StrawCheG29=min(min(TEMP_dataG29_StrawChe$logchange_allop-1.96*TEMP_dataG29_StrawChe$sd_allop, na.rm= TRUE),
min(TEMP_dataG29_StrawChe$logchange_symp-1.96*TEMP_dataG29_StrawChe$sd_symp, na.rm= TRUE))
ymax_StrawCheG29=max(max(TEMP_dataG29_StrawChe$logchange_allop + 1.96*TEMP_dataG29_StrawChe$sd_allop, na.rm= TRUE),
max(TEMP_dataG29_StrawChe$logchange_symp + 1.96*TEMP_dataG29_StrawChe$sd_symp, na.rm= TRUE))
StrawChe_G29 <- ggplot(data = TEMP_dataG29_StrawChe) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp),
width= 0.02, size = 0.2, alpha =1) +
geom_errorbarh(aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp),
height = 0.02, size = 0.2, alpha =1) +
geom_point(aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_text(x = -0.4, y = -0.3, label = eq_r(gen = 29, pair = "Strawberry_Cherry"), parse = TRUE, color="black") +
coord_cartesian(ylim = c(ymin_StrawCheG29, ymax_StrawCheG29),
xlim = c(ymin_StrawCheG29, ymax_StrawCheG29)) +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Strawberry vs. Cherry") +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(16, 17)) +
scale_color_manual(values = c("#BC3C6D", "#3FAA96")) +
theme_LO_sober
StrawChe_G29
legend_tradevide <- ggplot(data = data_logchange[data_logchange$Generation == "29",],
aes(x =logchange, y = logchange, color = Symp,fill = Symp, shape = Allop)) +
geom_point(size =2.5, fill = "white") +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(16, 15, 17)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober
legend_trade <- lemon::g_legend(legend_tradevide)
Slopeestimates_logchange_pairwise_errorbar <- cowplot::ggdraw() +
cowplot::draw_plot(CheCran_G7 + theme(legend.position = "none"),
x = 0.01, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(CranStraw_G7 + theme(legend.position = "none"),
x = 0.31, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(StrawChe_G7 + theme(legend.position = "none"),
x = 0.61, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(legend_trade, x = 0.85, y = 0.5, width = 0.1, height = 0.1) +
cowplot::draw_plot(CheCran_G29 + theme(legend.position = "none"),
x = 0.01, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot(CranStraw_G29 + theme(legend.position = "none"),
x = 0.31, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot(StrawChe_G29 + theme(legend.position = "none"),
x = 0.61, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot_label(c("Intermediate phenotyping step", "A", "B", "C", " ",
"Final phenotyping step", "D", "E", "F", " "),
x = c(0.30, 0.01, 0.30, 0.61, 0.92, 0.30, 0.01, 0.30, 0.61, 0.92),
y = c(1, 0.95, 0.95, 0.95, 0.95, 0.5, 0.45, 0.45, 0.45, 0.45),
hjust = c(-0.25, -0.25, -0.25, -0.25, -0.25, -0.75, -0.75, -0.75, -0.75, -0.75),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 14)
Slopeestimates_logchange_pairwise_errorbar
#
# #
#
cowplot::save_plot(file =here::here("figures", "FIG_Correlation.pdf"),
Slopeestimates_logchange_pairwise_errorbar,
base_height = 16/cm(1), base_width = 32/cm(1), dpi = 610)6 SA AND LOCAL ADAPTATION
6.1 Analysis fitness difference
# Add vector of weight
data_logchange$Inv_sum<-NA
data_logchange$Vector_sample<-NA
data_logchange[data_logchange$Generation=="7",]$Inv_sum <- 1 / (data_logchange[data_logchange$Generation=="7",]$sd_logchange^2)
data_logchange[data_logchange$Generation=="7",]$Vector_sample <- data_logchange[data_logchange$Generation=="7",]$Inv_sum /
sum(data_logchange[data_logchange$Generation=="7",]$Inv_sum,na.rm = TRUE)
data_logchange[data_logchange$Generation=="29",]$Inv_sum <- 1 / (data_logchange[data_logchange$Generation=="29",]$sd_logchange^2)
data_logchange[data_logchange$Generation=="29",]$Vector_sample <- data_logchange[data_logchange$Generation=="29",]$Inv_sum /
sum(data_logchange[data_logchange$Generation=="29",]$Inv_sum,na.rm = TRUE)
# Problem with NA (if one sd missing): replace by 0 (max value)
data_logchange$Vector_sample[is.na(data_logchange$Vector_sample)] <- 0
#####
##### G7
#####
lm_val_G7 = lm(logchange ~ Treatment + Line + SA + Treatment:Fruit_s,
weights = Vector_sample, data = data_logchange[data_logchange$Generation=="7",])
Fratio = anova(lm_val_G7)[3, 3]/anova(lm_val_G7)[4, 3]
pvalue = 1 - pf(Fratio, anova(lm_val_G7)[3, 1], anova(lm_val_G7)[4, 1])
pvalue## [1] 0.9777576Fratio## [1] 0.0009158865anova(lm_val_G7)[3, 1]## [1] 1anova(lm_val_G7)[4, 1]## [1] 3#####
##### G29
#####
lm_val_G29 = lm(logchange ~ Treatment + Line + SA + Treatment:Fruit_s,
weights = Vector_sample, data = data_logchange[data_logchange$Generation=="29",])
Fratio = anova(lm_val_G29)[3, 3]/anova(lm_val_G29)[4, 3]
pvalue = 1 - pf(Fratio, anova(lm_val_G29)[3, 1], anova(lm_val_G29)[4, 1])
pvalue## [1] 0.211062Fratio## [1] 2.513403anova(lm_val_G29)[3, 1]## [1] 1anova(lm_val_G29)[4, 1]## [1] 36.2 TEMP: A tej Analysis fitness
#####
##### G7
#####
# Pblm when Nb_adults= 0, it is impossible to calculate fitness log(0/20)
data_G7[data_G7$Nb_adults == 0,]## Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 85 Strawberry FR4 Strawberry 28 0 1 0
## 158 Cranberry CE1 Cherry 365 0 0 0
## 159 Cherry FR1 Strawberry 284 0 0 0
## 177 Cherry CE1 Cherry 249 0 1 0# correspond to 2 allopatric and 2 sympatric measurements (2 in CE1, 1 in FR4 et 1 in FR1)
# Remove Nb_adults = 0 because it is negligeable
data_G7_without0 <- data_G7[data_G7$Nb_adults!= 0,]
data_G7_without0$fitness <- log(data_G7_without0$Nb_adults/20)
lm_val_G7 = lm(fitness ~ Treatment + Line + SA + Treatment:Fruit_s, data = data_G7_without0)
Fratio = anova(lm_val_G7)[3, 3]/anova(lm_val_G7)[4, 3]
pvalue = 1 - pf(Fratio, anova(lm_val_G7)[3, 1], anova(lm_val_G7)[4, 1])
pvalue## [1] 0.9570523Fratio## [1] 0.003418531anova(lm_val_G7)[3, 1]## [1] 1anova(lm_val_G7)[4, 1]## [1] 3#####
##### G29
#####
# Pblm when Nb_adults= 0, it is impossible to calculate fitness log(0/20)
data_G29[data_G29$Nb_adults == 0,]## Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 5406 Cherry CEA Cherry 18 0 1 0
## 5494 Strawberry CRA Cranberry 56 0 0 0
## 5501 Strawberry CRA Cranberry 82 0 0 0
## 5535 Strawberry CEB Cherry 74 0 0 0
## 5555 Cherry CRE Cranberry 19 0 0 0
## 5563 Strawberry CRE Cranberry 113 0 0 0
## 5564 Strawberry CRE Cranberry 125 0 0 0
## 5629 Strawberry CRB Cranberry 106 0 0 0
## 5741 Cherry CEA Cherry 23 0 1 0
## 5769 Cranberry FRB Strawberry 23 0 0 0
## 5831 Cranberry CRA Cranberry 40 0 1 0
## 5922 Strawberry FRA Strawberry 82 0 1 0
## 5997 Strawberry CRD Cranberry 25 0 0 0
## 6059 Cherry CEA Cherry 187 0 1 0
## 6062 Strawberry CEA Cherry 195 0 0 0
## 6090 Strawberry FRB Strawberry 143 0 1 0
## 6111 Cherry FRB Strawberry 114 0 0 0
## 6240 Cranberry FRA Strawberry 125 0 0 0
## 6293 Cherry CEC Cherry 188 0 1 0
## 6301 Cherry CEC Cherry 163 0 1 0
## 6309 Cranberry CEC Cherry 199 0 0 0
## 6324 Strawberry CRD Cranberry 82 0 0 0
## 6325 Strawberry CRD Cranberry 72 0 0 0
## 6327 Strawberry CRD Cranberry 58 0 0 0
## 6330 Strawberry CRD Cranberry 59 0 0 0length(data_G29[data_G29$Nb_adults == 0 & data_G29$SA == 0,]$Nb_adults)## [1] 17length(data_G29[data_G29$Nb_adults == 0 & data_G29$SA == 1,]$Nb_adults)## [1] 8# correspond to 25 measures 17 allopatric and 8 sympatric measurements (2 in CE1, 1 in FR4 et 1 in FR1)
# Remove Nb_adults = 0 because it is negligeable
data_G29_without0 <- data_G29[data_G29$Nb_adults!= 0,]
data_G29_without0$fitness <- log(data_G29_without0$Nb_adults/20)
lm_val_G29 = lm(fitness ~ Treatment + Line + SA + Treatment:Fruit_s, data = data_G29_without0)
Fratio = anova(lm_val_G29)[3, 3]/anova(lm_val_G29)[4, 3]
pvalue = 1 - pf(Fratio, anova(lm_val_G29)[3, 1], anova(lm_val_G29)[4, 1])
pvalue## [1] 0.1733915Fratio## [1] 3.1628anova(lm_val_G29)[3, 1]## [1] 1anova(lm_val_G29)[4, 1]## [1] 3